{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a8cb3ce-3917-463b-ad80-5daa050807eb",
   "metadata": {},
   "source": [
    "# Notebook实例代码\n",
    "> 用于结合model中的其他数据进行便捷研究。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8cf981a1-fab4-42fb-947f-fa3b038261f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2c4e1b3-7dca-4318-9813-0be6beae3635",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29e7ce35-2110-4b4a-96b3-1a9c544b87e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10fa4408-063e-4456-bbc3-29685a1a9502",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
